LangGraph vs. CrewAI vs. Microsoft Agent Framework vs. n8n

A developer compares LangGraph, CrewAI, Microsoft Agent Framework, and n8n for building AI agents, recommending LangGraph for fine-grained control over agent state and retries, CrewAI for modeling specialist teams, Microsoft Agent Framework for organizations already on the Microsoft/Azure stack, and n8n for non-engineers or workflows needing broad business integrations. The developer notes that AutoGen is in maintenance mode and new projects should not start on it.

Pick LangGraph https://dev.to/go/langgraph when you need to control exactly how an agent moves between steps and retries. Pick CrewAI https://dev.to/go/crewai when you're modeling a team of specialist agents with clear roles and want that structure to stay readable. Pick Microsoft Agent Framework https://dev.to/go/microsoft-agent-framework when your org already lives in the Microsoft/Azure stack and needs one supported SDK across Python, C , and Java. Pick n8n https://dev.to/go/n8n when the people building and maintaining the workflow aren't primarily software engineers, or when the agent needs to sit next to 400 other business integrations you already run. None of these are mutually exclusive. A common production pattern we see in the Automations Cookbook https://dev.to/cookbook recipes is n8n orchestrating the business trigger a webhook, a form, a schedule and calling out to a LangGraph or CrewAI service for the part that actually needs multi-step reasoning. LangGraph models an agent as an explicit graph: nodes are steps, edges are transitions, and state is a typed object that flows between them. That gives you two things frameworks with more implicit control loops don't: you can pause, inspect, and resume execution at any node, and you can enforce that certain transitions never happen. Teams building on LangGraph tend to be doing so because they've already hit the wall with a simpler "just call the LLM in a loop" approach and needed real control over branching and retries. CrewAI's core abstraction is the crew: a set of agents, each with a role, a goal, and a backstory, working through a sequence of tasks. It reads closer to an org chart than a state machine, which makes it the easier framework to hand to a teammate who isn't deep in the codebase — the config genuinely describes what the system does. CrewAI has grown fast enough to become one of the most-starred agent frameworks on GitHub, and it now ships an enterprise tier aimed at teams that outgrew the open-source crew definition files. If you built on AutoGen, this is where that investment goes now. Microsoft merged AutoGen with Semantic Kernel https://dev.to/go/semantic-kernel into a single Microsoft Agent Framework in October 2025, and AutoGen itself is in maintenance mode — new projects shouldn't start on it. The pitch for Agent Framework is consistency: the same agent orchestration concepts across Python, C , and Java SDKs, wired into Azure AI Foundry and the rest of the Microsoft enterprise stack. It's the pragmatic default if procurement, security review, and long-term support already point you at Microsoft. n8n isn't trying to be an agent-reasoning framework — it's trying to be the workflow layer that triggers, sequences, and connects everything else, including calls out to LangGraph or CrewAI services. Its advantage is breadth: hundreds of pre-built integrations Slack, HubSpot, Google Sheets, Stripe that would otherwise be custom code in a pure-code agent stack. For teams where the actual agent logic is simple classify, summarize, route but the surrounding business process touches a dozen SaaS tools, n8n is usually the faster path to production — see the AI Workflow Automation guide https://dev.to/guides/ai-workflow-automation for patterns. Ask who maintains this after you ship it. If it's an ops or RevOps person, not an engineer, n8n's visual workflow wins even if it's less flexible. If it's an engineering team that needs fine-grained control over agent state and retries, LangGraph earns its complexity. If the mental model is "a team of specialists with different jobs," CrewAI keeps that structure honest as the system grows. If your organization already has Microsoft/Azure procurement, security, and support relationships in place, Agent Framework removes a category of approval friction the others don't. Q: Is AutoGen still worth learning in 2026? A: Only if you're maintaining an existing AutoGen system. Microsoft has moved new development to Agent Framework, and AutoGen is in maintenance mode. Q: Can I use n8n and LangGraph together? A: Yes — it's a common pattern. n8n handles triggers, integrations, and the surrounding business workflow; a LangGraph service handles the part of the task that needs stateful multi-step reasoning, called from an n8n HTTP Request node. Q: Which of these is easiest for a non-engineer to maintain? A: n8n, by a wide margin — its visual editor is built for that. CrewAI's role/task config is the next easiest to read without a deep coding background. Originally published on Automations Cookbook.